N2 - Structural optimization for damage tolerance under various unforeseen damage scenarios is computationally challenging. It couples non-linear progressive failure analysis with sampling based stochastic analysis of random damages. This work shows that analysis of damage tolerance depends on specification of damages, and optimizing a structure under one damage specification can be sensitive to other damages not considered. This work demonstrates the importance of understanding the underlying mechanics that provide damage tolerance in order to develop computationally efficient methods for optimization. Understanding features of load distributions in damage tolerant structures can result in efficient methods for optimization. To understand and identify these features, one compared and contrasted designs with varying degree of damage tolerance. A method to describe load distributions based on principal component analysis is presented. It is found that the number of dominant eigenvalues of principal components in a structure correlates with the number of alternate paths.

AB - Structural optimization for damage tolerance under various unforeseen damage scenarios is computationally challenging. It couples non-linear progressive failure analysis with sampling based stochastic analysis of random damages. This work shows that analysis of damage tolerance depends on specification of damages, and optimizing a structure under one damage specification can be sensitive to other damages not considered. This work demonstrates the importance of understanding the underlying mechanics that provide damage tolerance in order to develop computationally efficient methods for optimization. Understanding features of load distributions in damage tolerant structures can result in efficient methods for optimization. To understand and identify these features, one compared and contrasted designs with varying degree of damage tolerance. A method to describe load distributions based on principal component analysis is presented. It is found that the number of dominant eigenvalues of principal components in a structure correlates with the number of alternate paths.